__/ [derek] on Monday 07 November 2005 05:43 \__
> Dave: I'm not sure if your question is directed at me or not... but I
> can tell you what the voids are, if you're interested. The fracture
> surface that I mentioned above is of an adhesive of which we're trying
> to ascertain properties under dynamic load. For whatever reason, one
> batch of the adhesive that we got does not perform in the same manner
> as a different batch, and the easiest explanation seems to be that
> somewhere in the packaging process (of the adhesive), there was a fair
> amount of air trapped inside of the stuff. This causes, of course,
> 'voids' where there should have been adhesive when you look at the
> fracture surface. Does that make any sense?
Not only does it makes sense, but it makes the disucssion a more interesting
one.
You might also wish to try sci.image.processing where people can suggest
algorithms and recommend software packages.
> Roy: Thanks much for the idea... sorry though, I'm not too familiar
> with available graphics programs... do you have any suggestions for
> something I might use? It might take some fiddling to get it to work
> right, as there's a bit of black in the pictures that is not caused by
> the voids. Of course, I'm not sure what 'shade' of black it is, so it
> may be that, like you said, I can pick out certain parts of the
> histogram that correspond to the particular colors that I find in the
> voids.
>
> Anyways, thanks for the responses! I'll keep at it.
>
> ~Derke
If you have some 'interference' due to other smaller dark elements, try to
classify them. If you are going to deal with many such images, I also
suggest that you automate this. To classify based on size, try checking the
pixel neighbourhood and put a threshold on the number of neighbouring
pixels. If you save the image as a 24-bit (1 byte red, 1 byte green, 1 byte
blue) bitmap file, then you can easily scan the file and check the
thresholds while accumulating a count of dark pixels.
Roy
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